Digital twins in Chinese startups: Enhancing precision and efficiency in the entrepreneurial process

Digital Twin technologies (DTT)—virtual replicas of physical systems continuously updated through real-time data—are increasingly integrated into entrepreneurial activities. Nevertheless, their application in startups, especially in China, remains underexplored. This study analyzes DTT's capacity for precision and efficiency in entrepreneurial processes, drawing on mixed-methods data from 15 Chinese startups. We employed semi-structured interviews with founders and managers, alongside panel regression analysis of firm-level performance indicators (2020-2024). Results show that DTT enhances product iteration speed, reduces operational uncertainty, and improves decision-making accuracy. Whereas established firms primarily use DTT for operational optimization, startups in our sample deploy DTT as a strategic tool for experimentation, rapid learning, and risk mitigation. Theoretically, this study contributes to entrepreneurship scholarship by examining DTT through the lens of the resource-based view and dynamic capabilities, and by showing how a virtual-physical dyad reshapes the growth paths of early-stage firms. From a practical standpoint, these findings provide valuable insights for entrepreneurs and policymakers seeking to overcome bottlenecks to scaling through a digital infrastructure. This research is situated within the framework of China's digital economy strategy. It adds to the understanding of digital twin technologies and their societal and economic impacts in emerging entrepreneurial ecosystems. These findings highlight the strategic role of DTT in shaping innovation trajectories within emerging entrepreneurial ecosystems.